Sophisticated receiver architectures represent an integral piece in the collection of technologies enabling the ever-increasing data rates provided by current and developing wireless communication systems. As one example, various “turbo” receivers have been proposed, to boost performance. The well known turbo coding/decoding algorithms underlie the turbo receiver concept, and turbo receivers can yield significant performance gains. But they have significant computational complexity and add large delays in processing received communication signals.
One major source of complexity in a turbo receiver architecture is the multiple reuse of the equalizer. This has triggered the development of an alternative architecture, which may be called a “super receiver”, whereby the equalizer is used once, and a richer set of information than the traditional soft values is extracted from the equalizer. Then the richer information is used in interaction with the decoder to boost overall receiver performance.
The richer information can take the form of joint probabilities, extracted from a Maximum a posteriori (MAP) equalizer, in various implementations. See, for example, F. Glaschiodt, “Feedforward decoding using joint probabilities,” Master's thesis at the Royal Institute of Technology, Stockholm, 2000; A. Khayrallah and G. Bottomley, “Joint probability in demodulation and decoding,” Proceedings Conference on Information Sciences and Systems, 2001; and A. Khayrallah and G. Bottomley, “Methods and systems for extracting a joint probability from a map decision device and processing a signal using the joint probability information,” U.S. Pat. No. 6,798,852 (2004).
Even in a super-receiver architecture, it is important to reduce complexity further. One way to do that is to address the complexity of the MAP equalizer. MAP equalizers are, in some sense, optimal, but they have significant computational complexity. Such complexity has certain disadvantages in the mobile environment, given the constraints typically imposed by that environment on computational resources, speed, and power consumption.
As for simplifications to MAP-based processing, there are known “approximations” of the MAP equalizer, where one or more computational aspects of the “exact” (fully implemented, probability-based) MAP equalization are simplified, possibly at the expense of equalizer performance. In general, it is known to approximate MAP at least roughly by generating a type of soft value from a Maximum Likelihood Sequence Estimation (MLSE) process. One such approach is “cheap” Soft Output Viterbi Algorithm (SOVA). At a high level, cheap SOVA can be regarded as a rough approximation of MAP.
In order to use a cheap SOVA equalizer in a super-receiver architecture, it is necessary to extract an approximation to joint probability information from it. A method for extracting joint soft values, which are equivalent to an approximation of joint probabilities, is presented in A. Khayrallah, “Feedforward Receiver and Method for Reducing Inter-Symbol Interference by Using Joint Soft Values,” identified by U.S. patent application Ser. No. 12/342,470, and filed 23 Dec. 2008.
Simplified MAP (SMAP) is a more complex approximation of exact MAP than cheap SOVA, but one that usually yields better performance. Exact MAP relies on operations of the formc=−ln (e−a+e−b).  (Eq. 1)See, e.g., L. Bahl, J. Cocke, F. Jelinek, and J. Raviv, “Optimal decoding of linear codes for minimizing symbol error rate,” IEEE Trans. Inform. Thy., pp. 284-287, March 1974. In contrast, in one approach to SMAP, operations of the form shown in (Eq. 1) are replaced withc≈min(a,b).  (Eq. 2)In the SMAP context, a, b, and c represent metrics, whereas in exact MAP their negative exponentials represent probabilities. One benefit of shifting from the probability domain to the metric domain is a more manageable dynamic range. Another benefit is that the required iterations become the same as those of a MLSE. See, for example, A. Viterbi, “An intuitive justification and a simplified implementation of the MAP decoder for convolutional codes,” IEEE J-SAC, pp. 260-264, February 1998.
In order to use SMAP equalizer in a super-receiver architecture, it is necessary to extract an approximation to joint probability information from it. The present invention provides a method for extracting quantities called joint metrics from the SMAP equalizer, which can then be used in interaction with the decoder to boost overall performance.